Get Started with Data 360 Decision Guides

This section provides practical guidance for making critical architectural decisions about Salesforce Data 360, including provisioning strategies, multi-org connectivity, and integration patterns. Use these guides to design and implement effective data architectures that unify customer data and enable AI-powered features.

Data 360 Provisioning covers foundational decisions when provisioning Salesforce Data 360:

  • Home Org Selection: Decide whether to provision Data 360 in an existing production org or create a dedicated org, considering governance, compliance, and time-to-value.
  • Multi-Org Strategy: Choose between multiple independent Data 360 instances or using Data Cloud One to unify multiple Salesforce orgs, balancing autonomy, compliance, and cost.
  • Data Cloud One Architecture: Enable multiple Companion Orgs to share a single Data 360 instance with centralized governance, metadata sharing, and feature parity across orgs.
  • Data Sharing Between Data 360 Orgs: Use zero-copy data sharing between independent instances for regional compliance, business unit collaboration, or federated executive dashboards.
  • Provisioning Considerations: Factor in data residency, governance models, latency, integration complexity, cost, and AI readiness for enterprise-wide deployments.

Helps architects design provisioning strategies that ensure simplicity, enterprise consistency, compliance, and scalability for Customer 360, AI, and cross-platform innovation.

Data 360 Interoperability provides a framework for integrating Data 360 with external platforms and data lakes:

  • Data Ingestion Patterns: Real-time, streaming, and batch ingestion for creating governed canonical datasets ideal for Customer 360, compliance, and centralized governance.
  • Zero-Copy Data Federation: Live query, accelerated query (caching), and file federation methods to access external systems in real time without duplication, enabling personalization, dashboards, and AI/ML workloads.
  • Hybrid Approach: Combine governed ingestion for core datasets with zero-copy federation for freshness and scalability, supporting omni-channel engagement, Agentforce-driven actions, and enterprise analytics.
  • Decision Framework: Evaluate trade-offs in latency, cost, scalability, governance, and complexity to select the right integration pattern per data source.
  • Architect Playbooks: Industry-specific guidance, design practices, and cost considerations for finance, healthcare, retail, and telecom scenarios.

Guides architects in balancing data freshness, governance, and pipeline efficiency to build scalable, interoperable, AI-ready data architectures that support both operational and analytical workloads.